34 results about "Gene map" patented technology

The invention relates to the technical field of intelligent vocational ability analysis, and discloses a dynamic planning method for a vocational ability genetic map based on multi-modal data and federal learning. The method comprises the steps that multi-source heterogeneous data in occupational activities is collected, and a multi-modal data set is formed through data cleaning and standardization processing; a graph neural network technology is utilized to construct a correlation topology between capability elements, spatio-temporal context information is fused, a dynamically evolved vocational capability gene map is established, and the accuracy and interpretability of vocational capability analysis are improved. Multi-node cooperative training is realized by adopting a federated learning framework, data privacy security is ensured in combination with a differential privacy technology, and the problem of data islands is solved. A capability evolution path is simulated based on a three-dimensional visualization and digital twinning technology, an occupational development scheme is generated through an intelligent recommendation algorithm, and a real-time feedback optimization mechanism is established. According to the method, dynamic, visual and personalized vocational ability analysis is realized, and accurate decision support is provided for vocational planning.

The invention discloses an online stress monitoring device and method based on Modbus-RTU stress sensing and digital communication, and the method comprises the steps: collecting original stress data and a multi-dimensional environment parameter sequence through stress sensing nodes, constructing an exclusive environment gene map, generating a dynamic compensation model, and carrying out the environment coupling decoupling and self-adaptive correction of the original data; a periodic self-diagnosis process is executed in the monitoring process, signal impedance characteristics and output signal stability are analyzed, and an equipment state fault code is generated and actively uploaded through a Modbus-RTU protocol when abnormity occurs; triggering a cooperative monitoring mechanism based on the corrected stress measurement value, controlling adjacent nodes to enter a high-frequency sampling mode and generating a stress propagation dynamic cloud; meanwhile, a node performance baseline is established, and a gain self-regeneration process is started when performance degradation is detected. Accurate compensation and dynamic correction of stress data are realized, and the environmental adaptability, the operation reliability and the fault early warning capability of a monitoring system are remarkably improved.

The invention relates to the technical field of digital protection and exchange of cultural heritage, in particular to a visual information design optimization method for a non-genetic-cultural genetic map. Through fusion of multi-modal image features and cross-scale stratified analysis, a visual optimization system of a non-genetic-culture genetic map is constructed. RGB, depth and infrared multi-mode images are synchronously collected, and macroscopic, mesoscopic and microscopic multi-scale feature extraction is combined, so that deep association of pattern visual representation and cultural semantics is realized; a pre-training semantic model adaptive to the field and cross-modal correlation degree calculation are adopted, cultural symbols and pattern visual features in a spoken text of an inheritor are effectively fused, and the problem of semantic superficial layer of a traditional method is solved; based on an iterative verification mechanism of dynamic weight adjustment, through quantitative matching of feature attributes and semantic parameters, the consistency of culture genes with different scale features is ensured.

The invention discloses a synthetic lethal prediction method based on non-generality forgetting and low-rank interaction multi-mode adaptive fusion, and relates to the technical field of bioinformatics, computational biology and drug target screening. The method comprises the following steps: firstly, constructing a multi-view gene embedding expression based on a gene graph structure, a hypergraph structure and a knowledge graph; then constructing a difference vector and an interaction vector for any gene pair so as to characterize the difference and potential complementary relationship between the genes; multi-source information fusion is realized by using a deep learning model containing interactive attention, a forgetting gate and a multi-modal fusion mechanism; on this basis, multi-task optimization is carried out through joint classification loss, modal interaction loss and redundancy suppression loss, and optimal discrimination is realized through a dynamic threshold search strategy; and finally, outputting a sorting result of the candidate synthetic lethal gene pairs according to the prediction score. According to the method, the accuracy and generalization ability of synthetic lethal relationship prediction can be effectively improved, and a high-reliability calculation auxiliary tool is provided for anti-cancer drug target screening and gene therapy strategy design.

The invention relates to the field of model design, and provides an aircraft multi-objective optimization design method and system based on semanteme in order to improve the calculation efficiency of multi-objective balance. Firstly, original design parameters and design rules are obtained, obtained data are converted into semantic vectors through a semantic embedding function, and adjacency matrixes are obtained; generating a semantic gene map based on the semantic vector and the adjacent matrix, constructing a comprehensive objective function, and performing optimization solution based on the semantic gene map; and creating a target mapping function, and mapping an optimized result into a specific engineering target based on the target mapping function. By introducing a semantic modeling and cognitive enhancement mechanism, aircraft structure optimization is improved from traditional numerical drive to semantic drive, a prediction and search process is accelerated by using semantic space and cognitive reasoning, and global optimization and traceability are realized in combination with crowd intelligence evolution and a cross-domain hypernetwork; not only are efficiency and robustness improved, but also engineering feasibility and compliance credibility of a design result are ensured.

The invention relates to the field of data security, in particular to a network security intelligent detection method based on big data, and the method comprises the steps: collecting network flow data, a terminal system call sequence and a user operation behavior log, and carrying out the data fusion processing to generate a unified behavior event flow; selecting a key behavior event based on an information entropy threshold value, performing time alignment through a dynamic time warping algorithm, and constructing a behavior gene map containing a communication association gene, an operation sequence gene and a behavior time sequence gene; a dynamic behavior baseline model is established by using unsupervised learning, and gene mutation detection and alarm are realized by calculating the deviation degree of each gene dimension; the detection performance is evaluated based on the false alarm rate, model parameters are optimized through a negative feedback mechanism, and acknowledged attack features are stored in a sharable threat gene feature library through a positive feedback mechanism. According to the method, the detection accuracy is continuously improved through a closed-loop learning mechanism, and a self-adaptive safety protection system with self-optimization capability is constructed.

The invention belongs to the field of disease evolution trajectory prediction in precision medicine, and discloses a Parkinson's disease evolution prediction modeling method based on high-order gene communication tensor. According to the method, firstly, based on an intercellular communication mechanism, a high-order gene module communication tensor is constructed as spatial topological data, so that an information interaction mode between gene modules is described; secondly, a self-adaptive Transform module is designed to extract gene expression features and high-order space topology features at the same time; and finally, integrating multi-view interaction information between the high-order gene module communication tensor and the whole blood transcriptome data through a multi-view collaborative fusion mechanism to realize effective fusion and utilization of features. Experimental results show that compared with a traditional genetic map, the high-order space representation provided by the invention has finer resolution capability, a plurality of evaluation indexes are remarkably improved, and a new technical means is provided for accurate staging of Parkinson's disease.

The invention relates to a structural network-genetic map biological network model for predicting ischemic stroke and a construction method thereof, and the method comprises the steps: extracting and calculating seven multi-scale morphological features and pairwise Pearson correlation coefficients among the features from T1 weighted imaging data and diffusion tensor imaging data; constructing a 308 * 308 morphological similarity network matrix and a brain network module for identifying ischemic stroke neural dysfunction; 1782 sampling points are extracted from the Airy human brain map, and each sampling point comprises expression data of 10185 genes; the method comprises the following steps: mapping space coordinates of AHBA sampling points to a cortex package of a Desikan-Killiany map, carrying out normalization processing to output 308 * 10185 brain region gene-by-gene expression matrixes, and constructing a structural network-gene map biological network model for predicting ischemic stroke by adopting a partial least square regression method and a bootstrap method. Compared with the prior art, the model determines the specific molecular mechanism related to the phenotypic structure change of ischemic stroke injury, and the stroke occurrence probability is predicted according to the specific molecular mechanism.

Provided in the present application are a gene image data correction method and system, and an electronic device and a storage medium. The gene image data correction method comprises: inputting spatial coordinates and a gene expression feature signal value into a probability distribution model, so as to obtain a probability score of a background gene molecule belonging to a target cell; and according to the probability score, determining a cell to which the background gene molecule belongs. In the present application, with a rough understanding of a target region of a target cell, a probability score of a background gene molecule, with a cell to which the background gene molecule belongs being unknown, within a preset correction range belonging to a target cell is calculated, and the cell to which the background gene molecule belongs is determined according to the probability score, thereby reducing situations where errors in gene image data are large due to the diffusion of gene molecules, and increasing the correction efficiency; moreover, the present application does not rely on high-precision cell segmentation results, thereby enhancing the robustness of the correction scheme.

The application relates to a cancer driver gene explainable identification method based on trust calibration and prototype learning, and relates to the technical field of biological information identification. A gene graph is constructed by fusing a protein interaction network and gene multi-omics characteristics, and part of nodes are labeled. Label-aware message passing is performed through a trust calibration encoder, the neighborhood is split into a labeled part and a non-labeled part for independent calibration, and node embedding is adaptively fused. An angle margin prototype classifier is used to construct a class prototype on a hypersphere, the decision boundary is expanded, and a prediction result is output. A pivot node self-supervised regularizer is introduced, center nodes are screened from labeled driver genes, positive constraints are applied to neighbor non-labeled nodes, negative penalties are applied to non-neighbors, and a supervised boundary is maintained when non-labeled data is used. Finally, a structured explanation module is used to reuse the internal evidence of the model, a verifiable explanation is provided for prediction, and the unification of high precision and credible explanation is realized.

The invention belongs to the technical field of industrial Internet of Things and intelligent manufacturing, and particularly relates to an intelligent ranking method and system for garment production based on the Internet of Things. The method comprises the following steps: retrieving matched candidate genotypes from a production mode gene map library and analyzing the candidate genotypes into an initial ranking scheme; calculating a personnel-equipment-process collaborative adaptation degree coefficient of each station in the scheme; an optimization objective function fusing the collaborative adaptation degree, the historical environment adaptation degree and the production constraint is constructed, and an optimal ranking scheme is solved and output by adopting an improved genetic algorithm; and dynamically constructing a process flow potential energy field in production execution, and generating and executing a task shunting scheduling instruction according to a potential energy gradient. According to the method, the whole-process intelligent ranking from historical experience reuse and global multi-objective optimization to dynamic real-time scheduling is realized, the technical problems that production scheduling depends on experience, the optimization objective is single and dynamic response is insufficient in the flexible clothing production line are effectively solved, and the production scheduling efficiency, the production line balance rate and the system adaptive capacity are remarkably improved.

The invention provides a spatial transcriptome data clustering method based on modal conflict modulation, and relates to the technical field of biological information, and the method comprises the steps: carrying out the preprocessing of original spatial transcriptome data, and constructing a spatial diagram and a gene diagram; performing cross propagation learning of gene features and spatial features by using a graph convolutional network and taking the gene expression feature matrix, the spatial graph and the gene graph as input; in the training process, random masking is carried out on the space diagram, the gene is embedded and projected to a unit hypersphere, the reconstruction probability of masked connection is calculated based on the projected features, and a reconstruction loss function is constructed by using Jensen-Shannon divergence; and combining the reconstruction loss, the gene expression reconstruction loss and the spatial feature reconstruction loss to construct a total loss function training graph convolutional network. According to the technical scheme, the problem that the node distinguishing capability in the space transcriptomics data is weak is solved.

The application provides a transient working condition multi-parameter collaborative calibration method and system of a hybrid internal combustion engine, and the method comprises the following steps: acquiring a data stream in an on-board diagnostic system, and screening feature information of seven key dimensions from the data stream; creating a gene map matching engine, and inputting the feature information of the seven key dimensions into the gene map matching engine; comparing the feature information of the seven key dimensions with pre-stored working conditions in the gene map matching engine, obtaining an optimal working condition, and outputting a corresponding basic parameter package; combining knock energy spectrum characteristics and oxygen sensor offset, and performing matrix operation to optimize the basic parameter package, and obtaining a corrected parameter package; and performing boundary control on an ignition angle based on operating parameters of the internal combustion engine, and using the boundary-controlled ignition angle to constrain the ignition angle in the operation process of the internal combustion engine. The application can dynamically correct each parameter of the internal combustion engine.

The invention provides a method, a device and equipment for determining a setting value of an electric power low-voltage protection system, and the method comprises the steps: generating an equipment state sequence according to the operation state data of each low-voltage protection equipment, and enabling the equipment state sequence to represent the health states of the low-voltage protection equipment under different time sequences; compressing the equipment state sequence code to obtain a digital gene map code; matching the digital gene map code with the topological data of the power distribution network to obtain a code-topological mapping relation; determining a current-time characteristic requirement of each low-voltage protection device and an initial setting value of each low-voltage protection device according to the coding-topology mapping relation, and enabling the initial setting value to meet the current-time characteristic requirement; and performing simulation iteration adjustment on the setting value through a power distribution twinborn model. The coding-topology mapping relation can accurately reflect the personalized characteristics of the low-voltage protection equipment, so that the initial setting value of each piece of low-voltage protection equipment is determined, the actual operation condition of the equipment is better met, and the setting value scheme is more accurate.

The invention discloses a multi-gene interaction network prediction method and system based on a graph neural network. The method comprises the following steps: S1, acquiring multi-source biological data; s2, carrying out pretreatment; s3, constructing a gene map; s4, fusing the edge weight matrix and the node feature matrix in the gene map through a dynamic attention mechanism, and outputting a refined feature set; s5, acquiring an optimal hyper-parameter set by using a Bayesian optimization algorithm; s6, based on the gene map and the optimal hyper-parameter set, training the attention enhancement map neural network to obtain a multi-gene interaction network prediction model; and S7, predicting the interaction relationship of the unknown gene pair by using the multi-gene interaction network prediction model, and outputting a result. According to the method, the gene map is constructed through multi-source data fusion, the gene association information is enriched, the multi-head attention map neural network is introduced to automatically learn the interaction weight, the hyper-parameters are dynamically adjusted in combination with Bayesian optimization, and the prediction accuracy and model universality of the multi-gene interaction network are improved.

This invention belongs to the field of plant gene map-based cloning and molecular breeding, and relates to the application of the maize gene BRR1 and its functional site in regulating harvest index and yield traits. Through QTL mapping and fine mapping of a maize-Kesury grass population, this invention identified the major-effect QTL-HI1 regulating the harvest index and its key gene BRR1, and discovered that its upstream 9.1 kb insert is an enhancer with H3K27ac modification, which can physically interact with the BRR1 promoter and significantly upregulate gene expression. Knockout of BRR1 leads to a decrease in harvest index, biomass, and yield, and delayed flowering; overexpression of BRR1 increases harvest index and yield by 10–16%. This gene and its functional site are subject to strong selection during domestication and breeding, and can be used through transgenic technology or marker-assisted selection to achieve synergistic improvement of maize harvest index, yield, and flowering time, providing a new approach for high-yield molecular design breeding.

The application discloses a kind of metal mine broken grinding process simulation prediction method and system, it is related to grinding control technical field, including, collection full particle size distribution data, ore composition data and ore grade, transmission to edge computing node carries out data cleaning, extracts key feature parameters, generates ore gene atlas vector;Actual full particle size distribution data and equipment energy consumption data are compared with operating parameters, when any data exceeds preset prediction threshold, construct time-delay causal diagram, locate fault equipment, output equipment health degree report;According to equipment health degree report, using generative adversarial network generates adversarial sample injection digital twin model, reconstructs broken grinding process parameters to complete digital twin model self-calibration, and update pre-stored typical ore sample.The application improves the working condition adaptability of broken grinding process by dynamic gene matching mechanism.

The invention provides an educational resource sharing method and system for a big data platform, and relates to the technical field of educational resource data processing and sharing, and the method comprises the steps: constructing a dynamic resource genetic map which is formed by the vectorization fusion of static genes and dynamic performance characteristics of educational resources; matching the dynamic learning model of the user with the dynamic resource genetic map to generate a resource recommendation sequence; carrying out collaborative optimization on the dynamic resource gene map and a dynamic learning model of the user according to feedback data of the user on the resource recommendation sequence based on a reinforcement learning model; based on the optimized dynamic resource gene map and a dynamic learning model of the user, constructing a systematic learning path for the user, and realizing resource sharing according to the systematic learning path; the intelligent recommendation capability and the self-adaptive learning support of the system are enhanced, and the accuracy, the dynamism and the user experience optimization of educational resource sharing are promoted.

This application relates to the technical field of text plagiarism detection, and discloses a text plagiarism detection method combining gene mapping and zero-knowledge proof. The method includes: segmenting the text into plot exons and transition introns, extracting structural features through dependency parsing, decomposing it into three-stage plot units, constructing a directed acyclic graph with plot units as nodes and logical dependencies as edges, and assigning dynamic weights; calculating structural and semantic similarity using graph neural networks and BERT models respectively, analyzing narrative logic similarity, and weighted fusion to obtain a comprehensive similarity; hashing the text gene map segments and uploading it to the blockchain to form a four-layer association chain, recording structural changes through smart contracts, and designing a zero-knowledge proof verification mechanism; setting multi-level judgment thresholds, combining multi-dimensional similarity to perform suspected content and plot plagiarism judgments, and updating the model and threshold strategy based on user feedback. This application can meet the requirements of accurate, secure, and flexible text content structure plagiarism judgment.

The invention discloses an information stealing risk monitoring method and system based on big data, and the method comprises the following steps: data ecological perception and risk potential energy modeling: carrying out the automatic scanning of enterprise total data assets, generating a data fingerprint, and constructing a data gene map; based on the data genetic map, calculating three-dimensional risk potential energy of each data object, the three-dimensional risk potential energy including intrinsic potential energy quantified data intrinsic value, environmental potential energy quantified data exposure degree, association strength of associated potential energy quantified data with other high-value data, and associated potential energy quantified data exposure degree with other high-value data; according to the method, the data value and the exposure degree are quantified through inherent, environmental and associated three-dimensional risk potential energy, the potential energy is adjusted in real time in combination with event driving, data risk changes can be dynamically matched, risk misjudgment or omission caused by static evaluation is avoided, and risk perception better fits an actual business scene.

The invention relates to the technical field of text plagiarism detection, and discloses a text content structure plagiarism judgment method based on a genetic atlas, which comprises the following steps: segmenting a text into plot exons and transition introns, analyzing and extracting structural features through a dependency syntax, decomposing into three-stage plot units, constructing a directed acyclic graph taking plot units as nodes and logic dependency relationships as edges, and allocating dynamic weights; structural and semantic similarities are respectively calculated through a graph neural network and a BERT model, narrative logic similarities are obtained through analysis, and comprehensive similarities are obtained through weighted fusion; performing segmented hash on the text gene map, performing chaining to form a four-layer association chain, recording structural change through an intelligent contract, and designing a zero-knowledge proof verification mechanism; setting a multi-level judgment threshold, executing suspected content and plot plagiarism judgment in combination with multi-dimensional similarity, and updating a model and a threshold strategy based on user feedback. According to the method and the device, accurate, safe and flexible text content structure plagiarism judgment requirements can be met.

The present application relates to the technical field of bioinformatics and oncology, and particularly relates to a tumor driver gene identification system fusing multi-omics data and graph neural network, which comprises a data preprocessing module, a heterogeneity feature fusion module, a dynamic gene graph construction module, an improved graph neural network identification module, and a result verification and optimization module. Through standardizing the multi-omics raw data, the heterogeneity features are weighted and fused by using the attention mechanism, the dynamic gene co-expression network is constructed by combining the tumor development time series data and the clinical information, the driver gene identification is performed by using the graph attention network with embedded residual connection and multi-scale feature extraction, and the closed-loop optimization is realized by using the wet experiment data and the clinical sample feedback. The deep fusion and dynamic modeling of multi-dimensional data are realized, the accuracy, specificity and generalization ability of the driver gene identification are significantly improved, and the present application can be widely applied to the early diagnosis of tumors, the research and development of targeted drugs, and the formulation of individualized treatment plans.

The invention relates to a cost accounting method and system based on a cost essential manifold, and belongs to the field of cost accounting. The method comprises the steps of obtaining multi-dimensional features of each service item, obtaining a cost gene sequence of each service item based on the multi-dimensional features, and constructing an item cost gene map; obtaining an intrinsic manifold coordinate of each service item based on the cost gene sequence, and performing dimensionality reduction on the cost gene sequence to a cost intrinsic manifold; probability density is obtained based on the intrinsic manifold coordinates, and a probability potential energy field is generated; obtaining a maximum posterior probability cost rate based on the probability potential energy field, and carrying out cost accounting based on the maximum posterior probability cost rate; and establishing a dual-scale optimization target, and optimizing probabilistic potential energy field parameters and intrinsic manifold coordinates. According to the method, the cost gene map and the cost essential manifold are constructed, the probability potential energy field is introduced to dynamically integrate real-time data, multi-dimensional and self-adaptive cost accounting is realized, and the static property, simplification and inadaptability of the prior art are effectively overcome.

The invention relates to the technical field of computers, and discloses an immersive interaction control method and system based on a mobile NFC culture marking terminal, and the method comprises the steps: collecting and carrying out the structural processing of culture data; extracting a culture gene unit and constructing a culture gene map with weight and trigger conditions; binding the unique identifier of the mobile NFC terminal with the culture gene node; generating a parameterized interaction model containing a task structure, role logic and a state transition path; driving an interaction state machine to execute corresponding node behaviors through NFC physical triggering; and node parameters are dynamically updated based on the user interaction behavior data. The system comprises a culture data acquisition module, a gene modeling module, an interaction content generation module, an NFC terminal module, an identification module, a binding module, a control module, a behavior analysis module, a parameter updating module and a configuration instantiation module. According to the scheme, immersive interaction driven by cultural semantics, cross-scene rapid reconstruction and closed-loop adaptive optimization are realized.

This invention belongs to the fields of bioinformatics and computational biology, and specifically relates to a method, system, and storage medium for gene functional module mining based on tumor subtype supervision. The method includes the following steps: preprocessing breast cancer data and screening a unified gene set; calculating the difference in gene correlation coefficients before and after adding tumor samples in normal samples to construct a gene differential expression network; constructing a subtype-level gene graph structure through gene correlations, inputting it into a graph neural network to obtain an updated gene embedding representation; performing end-to-end training based on the gene embedding representation using differentiable minimum cut loss and subtype-supervised joint loss; and fusing the clustering results of samples within a subtype through majority voting to identify subtype-specific functional gene modules. This invention achieves the joint mining of cancer shared gene modules and subtype-specific gene modules, effectively improving the accuracy and biological interpretability of functional module identification.

This invention belongs to the field of disease evolution trajectory prediction in precision medicine, and discloses a method for predicting and modeling the evolution of Parkinson's disease based on high-order gene communication tensors. First, based on intercellular communication mechanisms, a high-order gene module communication tensor is constructed as spatial topological data to characterize the information interaction patterns between gene modules. Second, an adaptive Transformer module is designed to simultaneously extract gene expression features and high-order spatial topological features. Finally, a multi-perspective collaborative fusion mechanism is used to integrate the multi-perspective interaction information between the high-order gene module communication tensor and whole blood transcriptome data, achieving effective feature fusion and utilization. Experimental results show that, compared with traditional gene maps, the high-order spatial representation proposed in this invention has more refined resolution and significantly improves multiple evaluation indicators, providing a new technical means for the accurate staging of Parkinson's disease.

The invention provides a transient condition multi-parameter collaborative calibration method and system for a hybrid internal combustion engine, and the method comprises the steps: obtaining a data stream in a vehicle-mounted diagnosis system, and screening out the feature information of seven key dimensions from the data stream; creating a genetic map matching engine, and inputting the feature information of the seven key dimensions into the genetic map matching engine; comparing the feature information of the seven key dimensions with a pre-stored working condition in the genetic map matching engine to obtain an optimal working condition, and outputting a corresponding basic parameter packet; the basic parameter packet is optimized through matrix operation in combination with detonation energy spectrum characteristics and oxygen sensor offset, and a corrected parameter packet is obtained; based on the operation parameters of the internal combustion engine, boundary control is conducted on the ignition angle, and the ignition angle obtained after boundary control is used for restraining the ignition angle in the operation process of the internal combustion engine. Parameters of the internal combustion engine can be dynamically corrected.

The invention provides a multi-source data fusion-based collection and demolition whole-process digital management and control method and system, and the method comprises the steps: constructing a project genetic map feature space, and generating a typical project genotype knowledge base through DBSCAN clustering; after the optimal genotype of the new project is matched through the Wasserstein distance, a Transformer-Time2Seq model is activated to predict the fund use trend; when the deviation between the predicted expenditure and the actual expenditure exceeds a threshold value, triggering a context-aware gating mechanism to dynamically adjust the attention weight and the elastic coefficient of the cost subitem; generating a multi-scene budget adjustment suggestion package based on the correction result, and outputting a confidence ranking strategy through semantic matching; and all key data are stored through a block chain smart contract, so that tampering resistance and traceability are ensured.

The application relates to the field of model design, and aims to improve the calculation efficiency of multi-target balance, and provides a semantic-based aircraft multi-target optimization design method and system, wherein original design parameters and design rules are acquired first, the acquired data are converted into semantic vectors and an adjacency matrix is acquired through a semantic embedding function, a semantic gene map is generated based on the semantic vectors and the adjacency matrix, a comprehensive target function is constructed, and optimization solving is carried out based on the semantic gene map; a target mapping function is created, and the optimized result is mapped into specific engineering targets based on the target mapping function. Through the introduction of semantic modeling and cognitive enhancement mechanism, aircraft structure optimization is promoted from traditional numerical driving to semantic driving, semantic space and cognitive reasoning are used to accelerate the prediction and search process, and global optimization and traceability are realized in combination with swarm intelligence evolution and cross-domain super network; not only the efficiency and robustness are improved, but also the engineering feasibility and compliance reliability of the design result are ensured.

The invention relates to the technical field of low-code platforms, and particularly provides a low-code platform component intelligent generation method, system and device and a storage medium, and the method comprises the steps: receiving component generation demand information of a user; then semantic analysis is carried out on the demand, and a corresponding component template type and needed atomic power are determined by matching a pre-constructed component gene map; the genetic map comprises an atomic power library and a parameterized component template library. Thirdly, retrieving from a template library according to a matching result, performing combination to generate a component code skeleton, and filling the demand parameters into the skeleton so as to generate a target component code; and finally, outputting the code. According to the method, code synthesis is driven through a structured map and a deterministic rule, and the problem of black box uncertainty of a large model generation method is effectively solved.